http://confnews.um.ac.ir/images/41/conferences/icee2013/2478_2.pdf
Hybrid Method for Hand Gesture Recognition Based on Combination of Haar-Like and Hog Features
作者:
Ghafouri， S. and Seyedarabi， H.
关键词:
feature extraction；gesture recognition；gradient methods；HOG features；Haar-like features；false positive error rate；hand gesture recognition；histogram of oriented gradients；hybrid method；Classification algorithms
摘要:
In this paper a new method is proposed for hand gesture recognition. The proposed method increases hand gesture recognition rate and decreases false positive error rate by using combination of Haar-like and Histogram of Oriented Gradients (HOG) features. Also some new Haar-like features are proposed proportional to hand posture to solve major Haar-like problem that is high false positive error rate in hand posture recognition. These features improve recognition rate to 83%. The experiments showed that hybrid method can recognize hand gesture by 93.5% accuracy which is 25% higher than previous method, and decrease the false positive error from 92% to 8%.
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